Class: Rumale::Tree::GradientTreeRegressor

Inherits:
Base::Estimator show all
Includes:
Base::Regressor, ExtGradientTreeRegressor
Defined in:
rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb

Overview

GradientTreeRegressor is a class that implements decision tree for regression with exact gredy algorithm. This class is used internally for estimators with gradient tree boosting.

Reference

  • Friedman, J H., “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, 29 (5), pp. 1189–1232, 2001.

  • Friedman, J H., “Stochastic Gradient Boosting,” Computational Statistics and Data Analysis, 38 (4), pp. 367–378, 2002.

  • Chen, T., and Guestrin, C., “XGBoost: A Scalable Tree Boosting System,” Proc. KDD’16, pp. 785–794, 2016.

Instance Attribute Summary collapse

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(reg_lambda: 0.0, shrinkage_rate: 1.0, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ GradientTreeRegressor

Initialize a gradient tree regressor

Parameters:

  • reg_lambda (Float) (defaults to: 0.0) —

    The L2 regularization term on weight.

  • shrinkage_rate (Float) (defaults to: 1.0) —

    The shrinkage rate for weight.

  • max_depth (Integer) (defaults to: nil) —

    The maximum depth of the tree. If nil is given, decision tree grows without concern for depth.

  • max_leaf_nodes (Integer) (defaults to: nil) —

    The maximum number of leaves on decision tree. If nil is given, number of leaves is not limited.

  • min_samples_leaf (Integer) (defaults to: 1) —

    The minimum number of samples at a leaf node.

  • max_features (Integer) (defaults to: nil) —

    The number of features to consider when searching optimal split point. If nil is given, split process considers all features.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator. It is used to randomly determine the order of features when deciding spliting point.



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 52

def initialize(reg_lambda: 0.0, shrinkage_rate: 1.0,
               max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil)
  super()
  @params = {
    reg_lambda: reg_lambda,
    shrinkage_rate: shrinkage_rate,
    max_depth: max_depth,
    max_leaf_nodes: max_leaf_nodes,
    min_samples_leaf: min_samples_leaf,
    max_features: max_features,
    random_seed: random_seed || srand
  }
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature. The feature importances are calculated based on the numbers of times the feature is used for splitting.

Returns:

  • (Numo::DFloat) —

    (shape: [n_features])



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 25

def feature_importances
  @feature_importances
end

#leaf_weights ⇒ Numo::DFloat (readonly)

Return the values assigned each leaf.

Returns:

  • (Numo::DFloat) —

    (shape: [n_leaves])



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 37

def leaf_weights
  @leaf_weights
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 33

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.

Returns:



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 29

def tree
  @tree
end

Instance Method Details

#apply(x) ⇒ Numo::Int32

Return the index of the leaf that each sample reached.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Leaf index for sample.



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 106

def apply(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)

  Numo::Int32[*(Array.new(x.shape[0]) { |n| partial_apply(@tree, x[n, true]) })]
end

#fit(x, y, g, h) ⇒ GradientTreeRegressor

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model.

  • y (Numo::DFloat) —

    (shape: [n_samples]) The taget values to be used for fitting the model.

  • g (Numo::DFloat) —

    (shape: [n_samples]) The gradient of loss function.

  • h (Numo::DFloat) —

    (shape: [n_samples]) The hessian of loss function.

Returns:



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 74

def fit(x, y, g, h)
  x = ::Rumale::Validation.check_convert_sample_array(x)
  y = ::Rumale::Validation.check_convert_target_value_array(y)
  ::Rumale::Validation.check_sample_size(x, y)

  # Initialize some variables.
  n_features = x.shape[1]
  @params[:max_features] ||= n_features
  @n_leaves = 0
  @leaf_weights = []
  @feature_importances = Numo::DFloat.zeros(n_features)
  @sub_rng = @rng.dup
  # Build tree.
  build_tree(x, y, g, h)
  @leaf_weights = Numo::DFloat[*@leaf_weights]
  self
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the values.

Returns:

  • (Numo::DFloat) —

    (size: n_samples) Predicted values per sample.



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# File 'rumale-tree/lib/rumale/tree/gradient_tree_regressor.rb', line 96

def predict(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)

  @leaf_weights[apply(x)].dup
end